In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes.
References
[1]
Wang, C.; Thorpe, C.; Thrun, S.; Hebert, M.; Durrant-Whyte, H. Simultaneous localization, mapping and moving object tracking. Int. J. Robot. Res 2007, 26, 889–916.
[2]
V？gtle, T.; Steinle, E. Detection and recognition of changes in building geometry derived from multitemporal laserscanning data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2004, 35, 428–433.
[3]
Champion, N.; Everaerts, J. Detection of unregistered buildings for updating 2D databases. EuroSDR Off. Publ 2009, 56, 7–54.
[4]
Criminisi, A.; Pérez, P.; Toyama, K. Region filling and object removal by exemplar-based inpainting. IEEE Trans. Image Process 2004, 13, 1200–1212.
[5]
Wang, L.; Jin, H.; Yang, R.; Gong, M. Stereoscopic inpainting: Joint color and depth completion from stereo images. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA, 23–28 June 2008; pp. 1–8.
[6]
Engels, C.; Tingdahl, D.; Vercruysse, M.; Tuytelaars, T.; Sahli, H.; van Gool, L. Automatic occlusion removal from fa？ades for 3D urban reconstruction. Proceedings of Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium, 22–25 August 2011; pp. 681–692.
[7]
Barnes, C.; Shechtman, E.; Finkelstein, A.; Goldman, D. PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph 2009, 28, 24:1–24:11.
[8]
Konushin, V.; Vezhnevets, V. Automatic Building Texture Completion. Proceedings of Graphicon’2007, Moscow, Russia, 23–27 June 2007; pp. 174–177.
[9]
Rasmussen, C.; Korah, T.; Ulrich, W. Randomized View Planning and Occlusion Removal for Mosaicing Building Fa？ades. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), Edmonton, AB, Canada, 2–6 August 2005; pp. 4137–4142.
[10]
Bénitez, S.; Denis, E.; Baillard, C. Automatic production of occlusion-free rectified fa？ade textures using vehicle-based imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2010, 38, 275–280.
[11]
Xiao, J.; Fang, T.; Zhao, P.; Lhuillier, M.; Quan, L. Image-based street-side city modeling. ACM Trans. Graph 2009, 28, 114:1–114:12.
[12]
Ortin, D.; Remondino, F. Occlusion-free image generation for realistic texture mapping. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2005, 36. Part 5/W17, 7.
[13]
B？hm, J. A Multi-image fusion for occlusion-free fa？ade texturing. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2004, 35, 857–873.
[14]
Craciun, D.; Paparoditis, N.; Schmitt, F. Multi-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments. Comput. Vision Image Underst 2010, 114, 1248–1263.
Frueh, C.; Sammon, R.; Zakhor, A. Automated Texture Mapping of 3D City Models with Oblique Aerial Imagery. Proceedings of IEEE 2nd International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), Thessaloniki, Greece, 6–9 September 2004; pp. 396–403.
[17]
Li, Y.; Zheng, Q.; Sharf, A.; Cohen-Or, D.; Chen, B.; Mitra, N.J. 2D-3D Fusion for Layer Decomposition of Urban Fa？ades. Proceedings of IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 6–13 November 2011.
[18]
Matikainen, L.; Hyypp？, J.; Kaartinen, H. Automatic detection of changes from laser scanner and aerial imagedata for updating building maps. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2004, 5, 3413–3416.
[19]
Vu, T.T.; Matsuoka, M.; Yamazaki, F. LIDAR-Based Change Detection of Buildings in Dense Urban Areas. Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 20–24 September 2004; 5, pp. 3413–3416.
[20]
Bouziani, M.; Go？ta, K.; He, D.C. Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS J. Photogramm 2010, 65, 143–153.
[21]
Matikainen, L.; Hyypp？, J.; Ahokas, E.; Markelin, L.; Kaartinen, H. Automatic detection of buildings and changes in buildings for updating of maps. Remote Sens 2010, 2, 1217–1248.
[22]
Sch？fer, T.; Weber, T.; Kyrinovi？, P.; Záme？niková, M. Deformation Measurement Using Terrestrial Laser Scanning at the Hydropower Station of Gab？íkovo. Proceedings of INGEO 2004 and FIG Regional Central and Eastern European Conference on Engineering Surveying, Bratislava, Slovakia, 11–13 November 2004.
[23]
Lindenbergh, R.; Pfeifer, N. A Statistical Deformation Analysis of Two Epochs of Terrestrial Laser Data of a Lock. Proceedings of ISPRS 7th Conference on Optical 3D Measurement Techniques, Vienna, Austria, 3–5 October 2005; 2, pp. 61–70.
[24]
Van Gosliga, R.; Lindenbergh, R.; Pfeifer, N. Deformation analysis of a bored tunnel by means of terrestrial laser scanning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2006, 36, 167–172.
[25]
Hsiao, K.; Liu, J.; Yu, M.; Tseng, Y. Change detection of landslide terrains using ground-based LiDAR data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2004, 35, 617–621.
[26]
Girardeau-Montaut, D.; Roux, M.; Marc, R.; Thibault, G. Change detection on points cloud data acquired with a ground laser scanner. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2005, 36, 30–35.
[27]
Hyypp？, J.; Jaakkola, A.; Hyypp？, H.; Kaartinen, H.; Kukko, A.; Holopainen, M.. others Map Updating and Change Detection Using Vehicle-Based Laser Scanning. Proceedings of 2009 IEEE Joint Urban Remote Sensing Event, Shanghai, China, 20–22 May 2009; pp. 1–6.
Kang, Z.; Lu, Z. The Change Detection of Building Models Using Epochs of Terrestrial Point Clouds. Proceedings of 2011 IEEE International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), Xiamen, China, 10–12 January 2011; pp. 1–6.
[30]
McDonald, J.; Kaess, M.; Cadena, C.; Neira, J.; Leonard, J. Real-time 6-DOF multi-session visual SLAM over large-scale environments. Robot. Auto. Syst. 2012, doi:10.1016/j.robot.2012.08.008.
[31]
Aijazi, A.K.; Checchin, P.; Trassoudaine, L. Segmentation based classification of 3D urban point clouds: A super-voxel based approach with evaluation. Remote Sens 2013, 5, 1624–1650.
[32]
Souza, A.A.; Gon？alves, L.M. 3D Robotic Mapping with Probabilistic Occupancy Grids. Int. J. Eng. Sci. Emerg. Technol 2012, 4, 15–25.
[33]
Tversky, A. Features of similarity. Psychol. Rev 1977, 84, 327–352.
[34]
Besl, P.; McKay, N. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell 1992, 14, 239–256.
[35]
Vihinen, M. How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genomics 2012, 13, S2.
[36]
Hripcsak, G.; Rothschild, A.S. Agreement, the F-measure, and reliability in information retrieval. J. Am. Med. Inform. Assoc 2005, 12, 296–298.
